Preparation
Show code
library(tidyverse)
library(here)
#library(ggtext)
#library(spatstat.core)
#library(patchwork)
theme_set(theme_light(base_size = 11.5, base_family = "Open Sans"))
theme_update(
panel.grid.major = element_line(size = .3, color = "grey93"),
panel.grid.minor = element_blank(),
panel.grid.major.x = element_blank(),
strip.background = element_rect(fill = "grey60", colour = "grey60"),
strip.text = element_text(size = 14, face = "bold"),
axis.title.x = element_text(size = 14, margin = margin(t = 12)),
axis.title.y = element_text(size = 14, margin = margin(r = 12)),
axis.text = element_text(size = 12),
legend.title = element_text(size = 14, face = "bold"),
legend.text = element_text(size = 12),
plot.title = element_text(size = 24, face = "bold", margin = margin(5, 0, 5, 0)),
plot.subtitle = element_text(margin = margin(5, 0, 25, 0), size = 17),
plot.title.position = "plot",
plot.margin = margin(rep(12, 4))
)
## source functions to generate and visualize distributions
source(here("R", "src", "generate-distributions.R"))
Research Question
- Predictive power of the acoustic monitoring
Is the predictive power of the current acoustic monitoring sufficient enough to estimate the true number of killed bats?
Predictions
The predictive power of the acoustic monitoring for the extrapolation of the expected number of stroke victims is decreasing
in case the crossing bats are not distributed uniformly or randomly.
with increasing length of the rotor blades.
for species with high-frequency echo calls.
Open Questions
- wie die Dichte skalieren zwischen großen und kleinen Windrädern?
- z.B. von innen nach außen abhängig von relativer oder absoluter Distanz zum Mittelpunkt?
- globale oder lokale Anzahl an Fledermäusen konstant halten?
Inputs
We have the following parameters we can vary:
- length of rotor blades (
radius_rotor) - 60 m versus 33 m- determines area of wind turbine (
area_rotoraspi * radius_rotor^2)
- determines area of wind turbine (
- proportion of area covered by acoustic monitoring (
proportion_covered)- determines area of acoustic monitoring (
area_monitoredasarea_rotor * proportion_covered) - determines radius to detect bars (
radius_monitoredassqrt(area_monitored / pi))
- determines area of acoustic monitoring (
- number of bats simulated (
n)- could be either total (for now) or number monitored (maybe later)
Simulations
Generate Several Distributions
Show code
rds <- here("output", "data-proc", "simulation-runs-corr-trial.rds")
if (!file.exists(rds)) {
scenarios <- simulate_multiple_distributions(
runs = 200, ## repetitions per combination, also used for seeding
prop_monitored = seq(.05, .5, by = .05),
#n = c(100L, 250L, 500L, 1000L, 5000L, 15000L),
n = seq(100L, 500L, by = 100L),
skewness = c(1, 3, 5)
)
write_rds(scenarios, rds)
} else {
scenarios <- read_rds(rds)
}
scenarios_cleaned <-
scenarios %>%
## remove extremely skewed distributions
filter(!str_detect(distribution, "_5$")) %>%
group_by(n, prop_monitored, distribution) %>%
mutate(diff = prop_monitored - median(prop_n_monitored)) %>%
ungroup() %>%
mutate(
distribution = factor(
distribution,
levels = c(
"uniform", "random", "inner", "outer",
"bottom_1", "bottom_3", "top_1", "top_3"
)
),
prop_monitored_lab = paste0(prop_monitored * 100, "%"),
prop_monitored_lab = fct_reorder(prop_monitored_lab, prop_monitored)
)
Visualizations Bat Passes
Connected Error Bars
Distribution (col) x Monitored Area (color) x Passes (x)
Show code
scenarios_cleaned %>%
group_by(distribution, prop_monitored, n) %>%
summarize(mean = mean(prop_n_monitored), sd = sd(prop_n_monitored)) %>%
ungroup() %>%
mutate(
base = mean - prop_monitored,
min = base - sd,
max = base + sd
) %>%
ggplot(aes(n, base, color = prop_monitored, group = prop_monitored)) +
geom_hline(aes(yintercept = 0), color = "grey75", size = 1.2) +
geom_line(
aes(color = prop_monitored,
color = after_scale(colorspace::desaturate(colorspace::lighten(color, .6), .4))),
size = .7, show.legend = FALSE
) +
geom_pointrange(aes(ymin = min, ymax = max), size = .3) +
facet_wrap(vars(distribution), nrow = 1) +
scale_x_discrete(expand = c(.06, .06)) +
scale_y_continuous(expand = c(.012, .012), breaks = -5:5 / 10) +
scico::scale_color_scico(
palette = "bamako", end = .8, direction = -1, name = "Proportion\ncovered\nby AUD:",
breaks = seq(.05, .5, by = .05), labels = scales::percent_format(accuracy = 1)
) +
guides(color = guide_legend(keywidth = unit(.6, "lines"), keyheight = unit(1.2, "lines"))) +
labs(x = "Number of bat passes", y = "Deviation from expected proportion") +
theme(panel.spacing.x = unit(.5, "lines"), axis.text = element_text(size = 10),
legend.text = element_text(hjust = 1))
Boxplots
Distribution (col) x Monitored Area (rows) x Passes (x)
Show code
box <-
ggplot(scenarios_cleaned, aes(factor(n), prop_n_monitored)) +
geom_hline(aes(yintercept = prop_monitored), color = "grey75", size = .7) +
geom_boxplot(aes(color = diff), size = .4, outlier.shape = 1, outlier.size = .3) +
scale_y_continuous(labels = scales::percent_format(),
sec.axis = dup_axis(name = "Area covered by AUD", breaks = NULL, labels = NULL)) +
scale_color_gradient2(low = "firebrick", mid = "grey40", high = "firebrick",
limits = c(-.5, .5), guide = "none") +
labs(x = "Number of bat passes", y = "Proportion of passes monitored") +
theme(panel.spacing.y = unit(.65, "lines"), axis.text = element_text(size = 10),
axis.title.y.right = element_text(size = 16, color = "grey60", face = "bold", margin = margin(l = 12)))
## fixed y scale
box + facet_grid(prop_monitored_lab ~ distribution)
Show code
ggsave(here("plots", "bat_passes", "passes_recorded_boxplots_fixed.pdf"), width = 13.5, height = 11, device = cairo_pdf)
## free y scale
box + facet_grid(prop_monitored_lab ~ distribution, scale = "free_y")
Show code
colors <- c("dodgerblue", "firebrick")
scenarios_cleaned %>%
mutate(diff_passes = (n_monitored / prop_monitored) - n) %>%
bind_rows(
tibble(
distribution = factor("uniform", levels = levels(scenarios_cleaned$distribution)),
label = c("Overestimation", "Underestimation"),
prop_monitored_lab = factor("5%", levels = levels(scenarios_cleaned$prop_monitored_lab)),
diff_passes = c(125, -125),
color = colors
)
) %>%
ggplot(aes(prop_monitored_lab, diff_passes)) +
annotate(geom = "rect", xmin = -Inf, xmax = Inf, ymin = 0, ymax = Inf, fill = colors[1], alpha = .05) +
annotate(geom = "rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = 0, fill = colors[2], alpha = .05) +
geom_text(aes(label = label, color = color), hjust = 0, fontface = "bold", size = 6) +
geom_hline(yintercept = 0, size = .6, linetype = "31", color = "grey75") +
geom_boxplot(color = "grey45", size = .3, outlier.size = .6, outlier.alpha = .4, outlier.shape = 20) +
stat_summary(geom = "point", shape = 18, size = 3) +
facet_wrap(~distribution, nrow = 2) +
scale_y_continuous(expand = c(.02, .02), breaks = seq(-500, 1000, by = 250)) +
scale_color_identity() +
labs(x = "Area covered by AUD",
y = "Absolute difference of predicted versus expected bat passes") +#,
#title = "How much do the actual numbers differ from predicted bat passes?",
#subtitle = "Absolute difference of actual bat passes and monitored bat passes") +
theme(axis.text.x = element_text(size = 8))
Show code
ggsave(here("plots", "bat_passes", "passes_difference_boxplots_abs.pdf"), width = 12, height = 9, device = cairo_pdf)
scenarios_cleaned %>%
mutate(diff_passes = ((n_monitored / prop_monitored) - n) / n) %>%
bind_rows(
tibble(
distribution = factor("uniform", levels = levels(scenarios_cleaned$distribution)),
label = c("Overestimation", "Underestimation"),
prop_monitored_lab = factor("5%", levels = levels(scenarios_cleaned$prop_monitored_lab)),
diff_passes = c(.75, -.75),
color = colors
)
) %>%
ggplot(aes(prop_monitored_lab, diff_passes)) +
annotate(geom = "rect", xmin = -Inf, xmax = Inf, ymin = 0, ymax = Inf, fill = colors[1], alpha = .05) +
annotate(geom = "rect", xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = 0, fill = colors[2], alpha = .05) +
geom_text(aes(label = label, color = color), hjust = 0, fontface = "bold", size = 6) +
geom_hline(yintercept = 0, size = .6, linetype = "31", color = "grey75") +
geom_boxplot(color = "grey45", size = .3, outlier.size = .6, outlier.alpha = .4, outlier.shape = 20) +
stat_summary(geom = "point", shape = 18, size = 3) +
facet_wrap(~distribution, nrow = 2) +
scale_y_continuous(expand = c(.02, .02), breaks = seq(-1, 3, by = .5)) +
scale_color_identity() +
labs(x = "Area covered by AUD",
y = "Relative difference of predicted versus expected bat passes") +#,
#title = "How much do the actual numbers differ from predicted bat passes?",
#subtitle = "Relative difference of actual bat passes and monitored bat passes") +
theme(axis.text.x = element_text(size = 8))
Visualizations Fatalities
Fatalities vs bat passes — What really happens
Show code
## with variation
scenarios_cleaned %>%
filter(prop_monitored == .05) %>%
ggplot(aes(n, n_fatalities)) +
#geom_abline(slope = .01, intercept = 0, size = .6, color = "grey75") +
geom_jitter(alpha = .02, width = 20, height = 0) +
geom_quantile(quantiles = c(.25, .75), color = "dodgerblue3", size = .6) +
geom_quantile(quantiles = c(.5), color = "red", size = .9) +
facet_grid(~ distribution) +
scale_x_continuous(expand = c(.1, .1)) +
labs(x = "Number of bat passes (reality)",
y = "Number of fatalities") + #,
#title = "What really happens",
#subtitle = "Number of fatalities per number of bat passes (1% correlation + variation)") +
theme(panel.grid.major.y = element_blank(), axis.text.x = element_text(size = 10))
Show code
ggsave(here("plots", "fatalities", "fatalities_correlation_reality_var.pdf"), width = 13.5, height = 3.2, device = cairo_pdf)
## without variation
scenarios_cleaned %>%
filter(prop_monitored == .05) %>%
mutate(n_fatalities = round(n / 100)) %>%
ggplot(aes(n, n_fatalities)) +
#geom_abline(slope = .01, intercept = 0, size = .6, color = "grey75") +
geom_point(alpha = .02) +
geom_quantile(quantiles = c(0.25, 0.75), color = "dodgerblue3", size = .6) +
geom_quantile(quantiles = c(0.5), color = "red", size = .9) +
facet_grid(~ distribution) +
scale_x_continuous(expand = c(.1, .1)) +
labs(x = "Number of bat passes (reality)",
y = "Number of fatalities") + #,
#title = "What really happens",
#subtitle = "Number of fatalities per number of bat passes (1% correlation without variation)") +
theme(panel.grid.major.y = element_blank(), axis.text.x = element_text(size = 10))
Fatalities vs bat passes — What we observe
Show code
## with variation
## fixed x-scale
scenarios_cleaned %>%
ggplot(aes(n_monitored, n_fatalities)) +
#geom_abline(aes(slope = .01*prop_monitored, intercept = 0), linetype = "13", size = .6, color = "grey75") +
geom_point(alpha = .02) +
geom_quantile(quantiles = c(0.25, 0.75), color = "dodgerblue3", size = .6) +
geom_quantile(quantiles = c(0.5), color = "red", size = .9) +
facet_grid(prop_monitored_lab ~ distribution) +
scale_x_continuous(limits = c(0, 550), breaks = c(1, 1:5*100)) +
scale_y_continuous(sec.axis = dup_axis(name = "Proportion covered by AUD", breaks = NULL, labels = NULL)) +
labs(x = "Number of bat passes (monitored)",
y = "Number of fatalities") + #,
#title = "What is detected",
#subtitle = "Number of fatalities per number of monitored bat passes (1% correlation + variation)") +
theme(panel.grid.major.y = element_blank(), panel.spacing.y = unit(.65, "lines"), axis.text = element_text(size = 10),
axis.title.y.right = element_text(size = 16, color = "grey60", face = "bold", margin = margin(l = 12)))
Show code
ggsave(here("plots", "fatalities", "fatalities_correlation_monitored_var.pdf"), width = 16, height = 11.5, device = cairo_pdf)
## with variation
## free-ranging x-axis
scenarios_cleaned %>%
ggplot(aes(n_monitored, n_fatalities)) +
#geom_abline(slope = .01, intercept = 0, linetype = "13", size = .6, color = "grey75") +
geom_point(alpha = .02) +
geom_quantile(quantiles = c(0.25, 0.75), color = "dodgerblue3", size = .6) +
geom_quantile(quantiles = c(0.5), color = "red", size = .9) +
facet_grid(prop_monitored_lab ~ distribution, scales = "free_x") +
#scale_x_continuous(breaks = c(1, 1:5*100)) +
scale_y_continuous(sec.axis = dup_axis(name = "Proportion covered by AUD", breaks = NULL, labels = NULL)) +
labs(x = "Number of bat passes (monitored)",
y = "Number of fatalities") +#,
#title = "What is detected",
#subtitle = "Number of fatalities per number of monitored bat passes (1% correlation + variation); note the different x axis ranges") +
theme(panel.grid.major.y = element_blank(), panel.spacing = unit(.65, "lines"), axis.text = element_text(size = 10),
axis.title.y.right = element_text(size = 16, color = "grey60", face = "bold", margin = margin(l = 12)))
Show code
ggsave(here("plots", "fatalities", "fatalities_correlation_monitored_var_free.pdf"), width = 16, height = 11.5, device = cairo_pdf)
## without variation
scenarios_cleaned %>%
mutate(n_fatalities = round(n / 100)) %>%
ggplot(aes(n_monitored, n_fatalities)) +
#geom_abline(slope = .01, intercept = 0, linetype = "13", size = .6, color = "grey75") +
geom_point(alpha = .02) +
geom_quantile(quantiles = c(0.25, 0.75), color = "dodgerblue3", size = .6) +
geom_quantile(quantiles = c(0.5), color = "red", size = .9) +
facet_grid(prop_monitored_lab ~ distribution) +
scale_x_continuous(limits = c(0, 550), breaks = c(1, 1:5*100)) +
scale_y_continuous(sec.axis = dup_axis(name = "Proportion covered by AUD", breaks = NULL, labels = NULL)) +
labs(x = "Number of bat passes (monitored)",
y = "Number of fatalities") +#,
#title = "What is detected",
#subtitle = "Number of fatalities per number of monitored bat passes (1% correlation without variation)") +
theme(panel.grid.major.y = element_blank(), panel.spacing.y = unit(.65, "lines"), axis.text = element_text(size = 10),
axis.title.y.right = element_text(size = 16, color = "grey60", face = "bold", margin = margin(l = 12)))
Summaries (WIP)
Show code
scenarios_fat_pred <-
scenarios_cleaned %>%
mutate(
n_fatalities_expected = n / 100,
n_fatalities_predicted = n_monitored / 100 ,
#n_fatalities_predicted = n_monitored * (prop_monitored*100) / 100 ,
diff_fatalities = n_fatalities_expected - n_fatalities_predicted
)
scenarios_fat_pred %>%
ggplot(aes(n_fatalities_expected, n_fatalities_predicted)) +
geom_hline(yintercept = 0, size = .6, color = "grey75") +
geom_abline(slope = 1, intercept = 0, linetype = "13", size = .6, color = "grey75") +
geom_point(shape = 20, alpha = .0025, size = 2) +
geom_quantile(quantiles = c(0.25, 0.75), color = "dodgerblue3", size = .6) +
geom_quantile(quantiles = c(0.5), color = "red", size = .9) +
facet_grid(prop_monitored ~ distribution) +
labs(x = "Number of fatalities (expected, as 1% of all passes)",
y = "Number of fatalities (predicted, as 1% of passes detected by acoustic monitoring)",
title = "How good is the predicted fatality risk?",
subtitle = "Absolute difference between number of fatalities based on acoustic monitoring versus expected (1% correlation + variation)") +
theme(panel.grid.major.y = element_blank())
ggsave("./plots/scenarios_correlation_fatalities_exp_pred.pdf", width = 16, height = 16, device = cairo_pdf)
scenarios_fat_pred %>%
mutate(prop_monitored = as.factor(str_sub(prop_monitored, 2, nchar(prop_monitored)))) %>%
ggplot(aes(prop_monitored, diff_fatalities)) +
geom_boxplot(color = "grey60", size = .3, outlier.size = .4, outlier.alpha = .2, outlier.shape = 20) +
stat_summary(geom = "point", shape = 18, size = 3) +
facet_wrap(~distribution, nrow = 1) +
scale_y_continuous(expand = c(.015, .015)) +
labs(x = "Proportion covered by AUD",
y = "Absolute difference in fatalities\nexpected – predicted",
title = "How good is the predicted fatality risk?",
subtitle = "Absolute difference of actual bat fatalities and predicted fatalies based on monitoring") +
theme(axis.text.x = element_text(size = 8))
ggsave("./plots/scenarios_correlation_fatalities_difference_box.pdf", width = 18, height = 6, device = cairo_pdf)
scenarios_fat_pred %>%
mutate(prop_monitored = as.factor(str_sub(prop_monitored, 2, nchar(prop_monitored)))) %>%
ggplot(aes(prop_monitored, diff_fatalities / n_fatalities_expected)) +
geom_boxplot(color = "grey60", size = .3, outlier.size = .4, outlier.alpha = .2, outlier.shape = 20) +
stat_summary(geom = "point", shape = 18, size = 3) +
facet_wrap(~distribution, nrow = 1) +
scale_y_continuous(expand = c(.015, .015)) +
labs(x = "Proportion covered by AUD",
y = "Relative difference in fatalities\n(expected – predicted) / expected",
title = "How good is the predicted fatality risk?",
subtitle = "Relative difference of actual bat fatalities and predicted fatalies based on monitoring") +
theme(axis.text.x = element_text(size = 8))
ggsave("./plots/scenarios_correlation_fatalities_difference_prop_box.pdf", width = 18, height = 6, device = cairo_pdf)
scenarios_fat_pred %>%
mutate(prop_monitored = as.factor(str_sub(prop_monitored, 2, nchar(prop_monitored)))) %>%
ggplot(aes(prop_monitored, diff_fatalities)) +
geom_jitter(color = "grey90", height = 0, alpha = .1, width = .3, size = .4) +
geom_smooth(aes(group = 1), color = "grey40") +
stat_summary(geom = "point", shape = 18, size = 3) +
facet_wrap(~distribution, nrow = 1) +
scale_y_continuous(expand = c(.008, .008)) +
labs(x = "Proportion covered by AUD",
y = "Absolute difference in fatalities\nexpected – predicted",
title = "How good is the predicted fatality risk?") +
theme(axis.text.x = element_text(size = 8))
ggsave("./plots/scenarios_correlation_fatalities_difference_smooth_jitter.pdf", width = 18, height = 6, device = cairo_pdf)
scenarios_fat_pred %>%
mutate(prop_monitored = as.factor(str_sub(prop_monitored, 2, nchar(prop_monitored)))) %>%
ggplot(aes(prop_monitored, diff_fatalities)) +
ggbeeswarm::geom_quasirandom(color = "grey90", alpha = .5, width = .3, size = .4) +
geom_smooth(aes(group = 1), color = "grey40") +
stat_summary(geom = "point", shape = 18, size = 3) +
facet_wrap(~distribution, nrow = 1) +
scale_y_continuous(expand = c(.005, .005)) +
labs(x = "Proportion covered by AUD",
y = "Absolute difference in fatalities\nexpected – predicted",
title = "How good is the predicted fatality risk?") +
theme(axis.text.x = element_text(size = 8))
ggsave("./plots/scenarios_correlation_fatalities_difference_smooth_bee.pdf", width = 18, height = 6, device = cairo_pdf)
scenarios_fat_pred %>%
mutate(prop_monitored = as.factor(str_sub(prop_monitored, 2, nchar(prop_monitored)))) %>%
ggplot(aes(prop_monitored, diff_fatalities / n_fatalities_expected)) +
ggbeeswarm::geom_quasirandom(color = "grey90", alpha = .5, width = .3, size = .4) +
geom_smooth(aes(group = 1), color = "grey40") +
stat_summary(geom = "point", shape = 18, size = 3) +
facet_wrap(~distribution, nrow = 1) +
scale_y_continuous(expand = c(.005, .005)) +
labs(x = "Proportion covered by AUD",
y = "Relative difference in fatalities\n(expected – predicted) / expected",
title = "How good is the predicted fatality risk?",
subtitle = "Relative difference of actual bat fatalities and predicted fatalies based on monitoring") +
theme(axis.text.x = element_text(size = 8))
ggsave("./plots/scenarios_correlation_fatalities_difference_prop_smooth_bee.pdf", width = 18, height = 6, device = cairo_pdf)
Session Info
Show code
Sys.time()
[1] "2022-01-17 16:23:47 CET"Show code
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252
[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C
[5] LC_TIME=German_Germany.1252
system code page: 65001
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] here_1.0.1 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[5] purrr_0.3.4 readr_2.0.2 tidyr_1.1.4 tibble_3.1.6
[9] ggplot2_3.3.5 tidyverse_1.3.1
loaded via a namespace (and not attached):
[1] colorspace_2.0-2 ellipsis_0.3.2 class_7.3-19
[4] rprojroot_2.0.2 fs_1.5.0 rstudioapi_0.13
[7] listenv_0.8.0 farver_2.1.0 MatrixModels_0.5-0
[10] scico_1.3.0 prodlim_2019.11.13 fansi_0.5.0
[13] lubridate_1.8.0 xml2_1.3.2 codetools_0.2-18
[16] splines_4.1.2 downlit_0.4.0 cachem_1.0.6
[19] knitr_1.36 jsonlite_1.7.2 pROC_1.18.0
[22] caret_6.0-90 broom_0.7.11 dbplyr_2.1.1
[25] compiler_4.1.2 httr_1.4.2 backports_1.2.1
[28] assertthat_0.2.1 Matrix_1.3-4 fastmap_1.1.0
[31] cli_3.1.0 htmltools_0.5.2 quantreg_5.86
[34] tools_4.1.2 gtable_0.3.0 glue_1.4.2
[37] reshape2_1.4.4 Rcpp_1.0.7 cellranger_1.1.0
[40] jquerylib_0.1.4 vctrs_0.3.8 pdftools_3.0.1
[43] nlme_3.1-153 conquer_1.2.1 iterators_1.0.13
[46] timeDate_3043.102 xfun_0.27 gower_0.2.2
[49] globals_0.14.0 rvest_1.0.2 lifecycle_1.0.1
[52] future_1.22.1 MASS_7.3-54 scales_1.1.1
[55] ipred_0.9-12 ragg_1.1.3 hms_1.1.1
[58] parallel_4.1.2 SparseM_1.81 yaml_2.2.1
[61] memoise_2.0.1 sass_0.4.0 distill_1.3
[64] rpart_4.1-15 stringi_1.7.5 highr_0.9
[67] foreach_1.5.1 lava_1.6.10 matrixStats_0.61.0
[70] rlang_0.4.12 pkgconfig_2.0.3 systemfonts_1.0.3
[73] evaluate_0.14 lattice_0.20-45 recipes_0.1.17
[76] labeling_0.4.2 tidyselect_1.1.1 parallelly_1.29.0
[79] plyr_1.8.6 magrittr_2.0.1 R6_2.5.1
[82] generics_0.1.1 DBI_1.1.2 pillar_1.6.4
[85] haven_2.4.3 withr_2.4.3 survival_3.2-13
[88] nnet_7.3-16 future.apply_1.8.1 modelr_0.1.8
[91] crayon_1.4.2 utf8_1.2.2 tzdb_0.1.2
[94] rmarkdown_2.11 grid_4.1.2 readxl_1.3.1
[97] data.table_1.14.2 qpdf_1.1 ModelMetrics_1.2.2.2
[100] reprex_2.0.1 digest_0.6.29 textshaping_0.3.6
[103] stats4_4.1.2 munsell_0.5.0 bslib_0.3.1
[106] askpass_1.1